basicStatistics           package:fBasics           R Documentation

_B_a_s_i_c _S_t_a_t_i_s_t_i_c_s _S_u_m_m_a_r_y

_D_e_s_c_r_i_p_t_i_o_n:

     A collection of functions which compute basic statistical
     properties.  Missing functions in R to calculate skewness and
     kurtosis are added,  a function which creates a summary
     statistics, and functions to calculate  column statistcs. 

     The functions are:

        1  'skewness'                    Returns value of skewness,
        2  'kurtosis'                    Returns value of kurtosis,
        3  'basicStats'                  Computes an overview of basic statistical values,
        4  'rowStats', 'colStats'        Calculates row/column statistics,
        5  'rowAvgs', 'colAvgs'          Calculates row/column means,
        6  'rowVars', 'colVars'          Calculates row/column variances,
        7  'rowStdevs', 'colStdevs'      Calculates row/column standard deviations,
        8  'rowSkewness', 'colSkewness'  Calculates row/column skewness,
        9  'rowKurtosis', 'colKurtosis'  Calculates row/column kurtosis,
       10  'rowCumsums', 'colCumsums'    Calculates row/column cumulated Sums.

_U_s_a_g_e:

     skewness(x, ...)
     ## Default S3 method:
     skewness(x, na.rm = FALSE, ...)
     ## S3 method for class 'data.frame':
     skewness(x, ...)
     ## S3 method for class 'POSIXct':
     skewness(x, ...)
     ## S3 method for class 'POSIXlt':
     skewness(x, ...)

     kurtosis(x, ...)
     ## Default S3 method:
     kurtosis(x, na.rm = FALSE, ...)
     ## S3 method for class 'data.frame':
     kurtosis(x, ...)
     ## S3 method for class 'POSIXct':
     kurtosis(x, ...)
     ## S3 method for class 'POSIXlt':
     kurtosis(x, ...)

     basicStats(x, ci = 0.95)

     rowStats(x, FUN, na.rm = FALSE, ...) 
     rowAvgs(x, na.rm = FALSE, ...)
     rowVars(x, na.rm = FALSE, ...)
     rowStdevs(x, na.rm = FALSE, ...)
     rowSkewness(x, na.rm = FALSE, ...)
     rowKurtosis(x, na.rm = FALSE, ...)
     rowCumsums(x, na.rm = FALSE, ...)

     colStats(x, FUN, na.rm = FALSE, ...) 
     colAvgs(x, na.rm = FALSE, ...)
     colVars(x, na.rm = FALSE, ...)
     colStdevs(x, na.rm = FALSE, ...)
     colSkewness(x, na.rm = FALSE, ...)
     colKurtosis(x, na.rm = FALSE, ...)
     colCumsums(x, na.rm = FALSE, ...)

_A_r_g_u_m_e_n_t_s:

      ci: confidence interval, a numeric value, by default 0.95,  i.e.
          95 percent. 

     FUN: the statistical function to be applied. 

   na.rm: a logical. Should missing values be removed? 

       x: a numeric vector, or a matrix for column statistics. 

     ...: arguments to be passed. 

_V_a_l_u_e:

     'skewness', 'kurtosis' 
      returns the value of the statistics, a numeric value. 

     'basicsStats' 
      returns data frame with the following entries and row names:
     nobs, NAs, Minimum, Maximum , 1. Quartile, 3. Quartile, Mean,
     Median, Sum, SE Mean, LCL Mean, UCL Mean, Variance, Stdev,
     Skewness, Kurtosis. 

     'rowStats', 'rowAvgs', 'rowVars', 'rowStdevs',
      'rowSkewness', 'rowKurtosis', 'rowCumsum' 
       computes sample statistics by column. Missing values can be
     handled. 

     'colStats', 'colAvgs', 'colVars', 'colStdevs',
      'colSkewness', 'colKurtosis', 'colCumsum' 
       computes sample statistics by column. Missing values can be
     handled.

_N_o_t_e:

     R's-base package contains a function 'colMeans' with an additional
     argument 'dim=1'. Therefore, the function used  here to compute
     column means (averages) is named 'colAvgs'.

_A_u_t_h_o_r(_s):

     Diethelm Wuertz for this R-port.

_S_e_e _A_l_s_o:

     'colMeans', 'mean', 'median', 'var'.

_E_x_a_m_p_l_e_s:

     ## basicStats -
        xmpBasics("\nStart: Basic Statistics of log-Returns > ")
        # Data NYSE Composite Index:
        data(nyseres)
        basicStats(nyseres)  
          
     ## moments -
        xmpBasics("\nNext: Moments, Skewness and Kurtosis > ")
        # Mean, Variance:
        mean(nyseres)
        var(nyseres)
        # Skewness, Kurtosis:
        # Note, can handele data.frames:
        skewness(nyseres)
        kurtosis(nyseres)   

